Method and device for classifying an object in an image

ABSTRACT

The invention relates to method of classifying an object ( 10 ) in an image ( 9 ), the method comprising the steps of:
         defining an image area ( 11 ) located within the object ( 10 ) in the image ( 9 ),   decomposing the image area ( 11 ) into an array of subareas ( 13 ),   defining an array of aggregated pixel values by calculating, for each of the subareas ( 13 ), an aggregated pixel value of the respective subarea ( 13 ),   calculating a gradient array depending on differences between the aggregated pixel values of adjacent subareas ( 13 ),   analyzing the gradient array,   identifying, depending on a result of the analyzing step, the object ( 10 ) as belonging to a class of a predefined set of classes.       

     Furthermore, the invention relates to a device ( 4 ) for analyzing an object ( 10 ) contained in an image ( 9 ) and to a driver assistance system as well as to a vehicle ( 1 ) containing such a device ( 4 ).

The present invention relates to a method for classifying an object,such as a traffic sign, in an image as well as to a corresponding devicefor analyzing an object in an image. The invention further relates to adriver assistance system for providing information on classified trafficsigns to a driver of a vehicle, and to a vehicle equipped with such asystem.

Methods and devices for classifying an object in an image, i.e.identifying to which of a predefined set of classes (i.e. categories)the object belongs, are used in various technical areas. For example,object classification is used in the field of machine vision. Forinstance, modern driver assistance systems may be equipped with trafficsign recognition technology and be configured to detect and classifytraffic signs in a surrounding of a vehicle in order to assist thedriver in recognizing the information displayed on the traffic signs.Known issues with such systems, however, concern their robustness andefficiency, for instance, due to the sophisticated information displayedon the traffic signs or due to problems related to image noise.

For example, publication WO 2013/152929 A1 discloses a method to involvethe vehicle driver in improving the ability of a system to recognize atraffic sign by adding a learning unit into the system. The driver maythus give a feedback to the system which then updates its parameters toimprove its reliability. However, a learning unit significantlyincreases computational and memory storage requirements for the system.

Publication DE 10 2012 023 022 A1 discloses a method and system forrecognizing text appearing in the image data and of computing aprobability that words, numbers or abbreviations in the text arecontained by traffic signs. The system is aiming to discard falselydetected candidates, so as to provide more reliable information to thevehicle driver. However, text recognition is always interfered by thedifferences between various fonts, and as a result, it may not besufficiently robust.

Other traffic sign recognition methods have been disclosed, for example,in the publications CN 102956118 A, EP 1327969 A1 and DE 10 2012 213344A1.

The purpose of the present invention is to suggest a method and a devicefor classifying an object in an image which are robust and fast andwhich require only relatively little memory capacity.

This problem is solved by a method for classifying an object in an imageaccording to claim 1 and by a device for analyzing an object containedin an image according to claim 11. Particular embodiments of theinvention are subject-matter of the dependent claims.

The suggested method for classifying an object in an image comprises thesteps of:

-   -   defining an image area located within the object in the image,    -   decomposing the image area into an array of subareas,    -   defining an array of aggregated pixel values by calculating, for        each of the subareas, an aggregated pixel value of the        respective subarea,    -   calculating a gradient array depending on differences between        the aggregated pixel values of adjacent subareas,    -   analyzing the gradient array,    -   identifying, depending on a result of the analyzing step, the        object as belonging to a class of a predefined set of classes.

According to the suggested method, the pixel values of the pixels in theimage area are aggregated for each of the subareas to reduce the amountof image data for the further analysis without, however, losing too muchof useful image information. It has been found that the informationcontained in the gradient array defined based on the array of aggregatedpixel values is still sufficient to be useful for the classification ofthe object. In fact, as is described further below, the step ofanalyzing the gradient array may involve steps that reduce theinformation contained in the gradient array even further.

The image is typically a digital image which is defined by imagedata.Each pixel of the image may have, for example, a pixel value, suchas a brightness value and/or a colour value. Initially, the image (i.e.the image data) may optionally be pre-processed, for instance, byapplying a scaling algorithm and/or by applying a colour transformationalgorithm to the image data. Additionally or alternatively, thepre-processing of the image may include applying a noise reductionalgorithm to reduce noise in the image data.

In order to define the above-mentioned image area, it may be necessaryto first detect and/or locate the object in the image. The method maythus include applying an algorithm for detecting and/or locating theobject in the image. For the purpose of defining the image area, it maybe sufficient to locate an outer boundary of the object in the image.For instance, the image area may be defined such that it is locatedwithin an area defined by an outer boundary of the object in the image.

Typically, the array of subareas is defined such that each subarea hasnot more than two adjacent subareas and, correspondingly, the gradientarray may be defined as a linear or one-dimensional array. In the stepof decomposing the image area into the subareas, each of the subareasmay, for example, be labelled with an index n with 1≦n≦N, wherein Ndenotes the total number of the subareas and wherein two neighbouringsubareas have successive indices, i.e. the indices n and n+1, forinstance. Thus, the step of defining the array of aggregated pixelvalues may, actually, be a step of defining a vector of aggregated pixelvalues and assigning the aggregated pixel values of the subareas tocomponents of the vector of aggregated pixel values. Then, typically,the vector of the aggregated pixel values has N components such thatthere is a 1:1-correspondence between these vector components and thesubareas. Preferably, the components of the vector of the aggregatedpixel values are ordered such that the n-th component of this vectorcorresponds to the n-th subarea. The gradient array may, in this case, agradient vector having N−1 components, wherein its components may beordered such that the n-th component of the gradient vector is definedas the difference between the nth and the (n+1)-th subarea (or as thedifference between the n-th and the (n+1)-th component of the vector ofaggregated pixel values). More generally, the gradient array may beobtained by calculating, for each pair of adjacent subareas among theabove-mentioned subareas of the image area, a difference between theaggregated pixel values of the respective pair of subareas. The tusobtained differences may be assigned to the different indices of thegradient array so that the components of the gradient array are definedas those differences. Of course, the differences may also weighted usingany constant or properly defined factor.

The aggregated pixel values of the subareas may be obtained by summingup pixel values of pixels contained in the respective subarea, i.e. byadding the pixel values of all pixels contained in the subarea.Alternatively, a weighted sum might be used as well.

The method may, in particular, be used to classify traffic signs or tocheck whether the object is a traffic sign and/or whether it belongs toa particular type of traffic signs. In other words, the object in theimage may be a traffic sign or a candidate for a traffic sign, whereinthe predefined set of classes includes a set of classes of traffic signsor at least one class of traffic signs. One of the classes may be theclass of all other objects, i.e. of all objects that are no trafficsigns or that do not belong to a particular class of traffic signs.

The method turns out to be particularly useful it the set of classes oftraffic signs includes at least one class of speed limit signs, each ofthe speed limit signs of this class displaying a number with one-digitor two-digit or three-digit number. Usually, the class of speed limitsigns will contain only numbers with two or three digits, a last digitof this number typically being zero or in some cases zero or five. Ifthis class contains three-digit numbers, a first of the three-digitnumbers is typically one. This makes it easy to identify an object asbelonging to this class by just analyzing the gradient array. To be morespecific, each one of the speed limit signs of this class may, forexample, displayone of the following set of numbers: “10”, “20”, “30”,“40”, “50”, “60”, “70”, “80”, “90”, “100”, “110”, “120”, “130”, and“140”.

If an object is identified as belonging to the at least one class ofspeed limit signs, the method may further comprise further analyzing theobject for identifying the number displayed on this particular speedlimit sign. This is, in particular, useful if the class or any of theclasses of speed limit signs contains more than one speed limit sign inorder to find out the actual speed limit represented by the number onthe sign. This can be done by a method of the same type as theclassifying method described here using different definitions for theclasses, the image area, the subareas and/or the analyzing step.Alternatively, other known image processing methods may be used forreading the speed limit.

In this context, it is to be mentioned that another image processingstep may be performed before the steps of the classifying methoddescribed here in order to find the object to be classified as acandidate for a traffic sign. This can be done, for example, be means ofa circle detection algorithm. The method may thus include a step ofdetecting and/or localizing traffic signs or candidates for trafficsigns in the image which are then classified used by the steps of thesuggested classifying method.

In typical embodiments, the image area is or comprises a horizontalband. This band may, e.g., be rectangular. Alternatively, the area mayhave a circular of an elliptical or a completely different shape. It maybe helpful if the image area is defined such that it extends over atleast 50% of a total width of the object—i.e. 50% of an area defined bythe object—and/or that a height of the image area is less than 20% of atotal height of the object and/or that the image area includes a centerof the object in the image.

As for the subareas of the image area, they may be defined, for example,as vertical strips. Normally, these strips will all have the same size.Alternatively, the area may be cut into slices of different shape and/ororientation, each of the slices being defined as one of the subareas.

Depending on the respective object in the image and on the definition ofthe image area and the classes, one or more characteristic features mayoccur in the image area depending on whether the object belongs to aparticular class or not. If one of these features is present in theimage area, it also becomes manifest in the array of aggregated pixelvalues and in the gradient array such that it can be determined byanalyzing the gradient array. The occurrence of one or more of thesefeatures in the image area may thus be used as an indicator for thebelonging of the respective object in the image to one or more of theclasses of the predefined set of classes.

To find characteristic features of the object in the image area, theanalyzing step may include comparing gradient values of the gradientarray with at least one threshold value, defining a feature array bymapping the gradient values onto a smaller set of values depending onwhether the respective value of the gradient array is larger or smallerthan the at least one threshold value, and analyzing the feature array.This may be done, for example, using one positive and one negativethreshold value, the smaller set of values consisting of three differentvalues only, one of them (+1 e.g.) representing gradient values whichare larger than the positive threshold value, one of them (−1 e.g.)representing gradient values which are smaller than the negativethreshold value, and one of them (0 e.g.) representing all othergradient values. In addition or alternatively, positions of local maximaor minima of the gradient array may be determined and used for analyzingthe gradient array.

In any case, the analyzing step may include comparing features of thegradient array with a predefined set of features. Typically, this isdone by comparing the feature array and/or the position of local maximaor minima of the gradient array with reference data.

The predefined set of features may be obtained from ground truth datausing a training set of objects. The training set of objects may, e.g.,comprise traffic signs including speed limit signs of the type mentionedabove.

In the typical example of identifying speed limit signs, thecharacteristic features of the image area may include two first regionsconsisting of pixels which have large pixel values, wherein the twofirst regions each have a predefined first width, and, located inbetween the two first regions and having a predefined second width whichis greater than the first width, a second region consisting of pixelswhich have small pixel values, wherein the two first regions and thesecond region are found in a right half of the image area. Thesecharacteristic features may be an indicator that the object is a trafficsign displaying a “0” in the right half, which is a hint suggesting thatthe traffic sign belongs to the class of speed limit signs.

These characteristic features may become manifest in the gradient array,for instance, as follows. Two first sequences of components of thegradient array with small gradient values (corresponding to the abovementioned two first regions) will be seen, wherein at the beginning ofeach one of these two first sequences there is at least one component ofthe gradient array which has a large positive gradient value (e.g. abovea predefined positive threshold) and which results in a local maximum ofthe gradient array, and wherein at the end of each one of these twofirst sequences there is at least one component of the gradient arraywhich has a large negative gradient value (e.g. below a predefinednegative threshold) and which results in a local minimum of the gradientarray, wherein each of the two sequences have a predefined first width,and wherein, located in between the two sequences and having apredefined second width which is greater than the first width, there isa second sequence of components with small gradient values(corresponding to the above mentioned second region). The components ofthe two first sequences and of the second sequence will be found in theright half of the image area if the image area is defined as ahorizontal band at a central level of the object or traffic signcandidate.

A useful device for analyzing an object contained in an image maycomprising a data processing unit which is configured for performing thesteps of the method described above. In particular, the data processingunit may be configured to perform the steps of:

-   -   defining an image area located within the object in the image,    -   decomposing the image area into an array of subareas,    -   defining an array of aggregated pixel values by calculating, for        each of the subareas, an aggregated pixel value of the        respective subarea,    -   calculating a gradient array depending on differences between        the aggregated pixel values of adjacent subareas,    -   analyzing the gradient array,    -   identifying, depending on a result of the analyzing step, the        object as belonging to a class of a predefined set of classes.

A driver assistance system may comprise, in addition to this device, atleast one camera for generating images of a surrounding of a vehicle,and a driver information unit, wherein the data processing unit iscoupled to the at least one camera to receive the images from the atleast one camera and wherein the driver information unit is coupled tothe data processing unit and configured to provide information on theobjects classified as traffic signs and/or speed limit signs to a driverof the vehicle. In a vehicle which is equipped with this driverassistance system, the at least one camera may be installed to capturethe surrounding of the vehicle in front of the vehicle, for examplebehind a windscreen, so that traffic signs will be sign in the imagescaptured by the camera.

In the following, exemplary embodiments of the invention are describedin more detail referring to the FIGS. 1 to 6. There are shown in

FIG. 1 a perspective view of a vehicle with a driver assistance system,

FIG. 2 a block diagram illustrating an embodiment of the driverassistance system,

FIG. 3 a flow diagram illustrating steps of a method for assisting adriver of the vehicle, which may be performed by the driver assistancesystem of FIG. 2,

FIG. 4 a speed limit sign in an image,

FIG. 5 an image region of the image shown in FIG. 4, and

FIG. 6 a diagram showing values of components of a gradient vectorobtained using an embodiment of the suggested method.

In all figures, similar or identical features are marked with the samereference signs. A list of reference signs is provided below.

FIG. 1 shows a schematic representation of a vehicle 1. A driverassistance system is installed in the vehicle 1. In FIG. 1, a camera 2of the driver assistance system is shown which is installed in thevehicle 1 in a position behind a front screen of the vehicle 1. In thisway, digital images of a surrounding of the vehicle 1 in front of thevehicle 1 can be generated by means of this camera 1. The driverassistance system of the vehicle 1 may include further cameras (notshown in FIG. 1) mounted in the vehicle 1 at further positions, forexample at a right side and a left side of the vehicle, such that imagesof the surrounding on both sides of the vehicle 1 may additionally begenerated by means of these further cameras.

FIG. 2 shows an embodiment of the driver assistance system 3, which maybe installed in the vehicle 1. The driver assistance system 3 includesthe at least one camera 2 and a device 4 for processing images. Thedevice 4 is configured to receive signals from the camera 2. The signalsrepresent the images captured by the camera 2 and include image data,such as pixel values of those images. The device 4 includes an imagedata processing unit 5 which is configured to process the images, i.e.the image data. More specifically, the data processing unit 5 isconfigured to detect and locate objects that are candidates for trafficsigns—this can be done, for example, by means of a circle detectionalgorithm—and to further classify them as speed limit signs, othertraffic signs, and other object that turn out not to be traffic signs.Moreover, the processing unit is configured to recognize the specificspeed limit displayed on any object classified as speed limit sign, i.e.to recognize the number “10”, “20”, “30”, “40”, “50”, “60”, “70”, “80”,“90”, “100”, “110”, “120”, “130”, or “140” displayed on the respectivespeed limit sign. To do so, the data processing unit 5 is configured toperform a method of classifying objects in the images which is describedin detail below. For that purpose, the data processing unit 5 maycomprise one ore more electronic data processors and one or more datastorage units.

The driver assistance system 3 further includes a driver informationunit 6 which is configured to provide information on the detected andclassified traffic signs to a driver of the vehicle 1 based on signalsreceived from the device 4. In the present example, the driverinformation unit 6 comprises a display 7 and a loudspeaker 8. Thedisplay 7 may, for instance, be a head-up display configured to displaythe information on the front screen of the vehicle in which the system 3is installed.

FIG. 3 shows a flow diagram which represents method steps of anembodiment of the method of classifying an object in an image. Forinstance, the object in the image may be a traffic sign or a candidateof a traffic sign. These method steps may be performed, for instance, bymeans of the driver assistance system 3 shown in FIG. 2. The descriptionof the method also refers to FIG. 4, which shows a simplified digitalimage 9 as it may be captured by the camera 2 and processed by thedevice 4 of the driver assistance system 3.

In step S1, a digital image, such as the image 9 shown in FIG. 4, isgenerated by the camera 2 and received by the data processing unit 5.Typically, this image 9 is a single frame of a video produced by thecamera 2. In the present example shown in FIG. 4, the image 9 containsan object 10.

In step S2, the data processing unit 5 pre-processes the image 9 byapplying a scaling algorithm and a color transformation algorithm, forinstance.

In step S3, the object 10 is detected and identified as a candidate of atraffic sign. In the present case, step S3 includes applying a circledetection algorithm or an ellipse detection algorithm for detecting andidentifying candidates for traffic signs in the image 9.

In step S4, an embodiment of the suggested classifying method whichincludes the steps X1 to X5 described below is performed to furtherclassify the identified candidates for traffic signs, such as object 10,to either belong to the class of speed limit signs or to belong to theclass of all other traffic signs or to a class of objects that actuallyare not traffic signs. In the present embodiment, each speed limit signof the class of speed limit signs displays one of the following set ofnumbers: “10”, “20”, “30”, “40”, “50”, “60”, “70”, “80”, “90”, “100”,“110”, “120”, “130”, and “140”. Furthermore, for those objects that areidentified as speed limit signs, the actual speed limit displayed on thespeed limit sign is identified.

The object 10 defines an area 12 in the image 9. In step X1, an imagearea 11 located within the area 12 is defined. FIG. 4 illustrates howthis is done. In the present case, the traffic sign shown in FIG. 4 is aspeed limit sign which includes an outer ring 17. The area 12 might bedefined such that it is restricted to a center area bounded by thisannular ring 17. Alternatively, the area 12 may be defined as the totalarea of the object 10 so that it is defined by outer edges of the object10.

In the present case, the image area 11 may be defined such that itextends over approximately ⅔ of a total width of the area 12 defined byouter edges of the object 10 or, alternatively, over approximately thetotal width of the area 12 defined within the outer ring 17 of theobject 10. Furthermore, the image area 11 may be defined such that itextends over less than 10% of a total height of the area 12. In thepresent case, the image area 11 is defined such that its width is morethan 10 times greater than its height. Furthermore, the image area 11 isdefined such that it covers a center 18 of the area 12 defined by theobject 10. The image area 11 has, in the present case, a rectangularshape. Alternatively it could also be shaped as a parallelogram, as acircle, or as an ellipse, for example.

In step X2, the image area 11 is decomposed into an array of subareas 13as shown in FIG. 5. The subareas 13 are defined such that each pixel ofthe image area 11 is contained in exactly one of these subareas 13. Inparticular, the subareas 13 do not intersect or overlap each other andcover the image area 11 completely. The subareas 13 of the image area 11are shaped, in this embodiment of the method, as narrow vertical strips,wherein each of the strips has a uniform width of one pixel, a length ofthe subareas 13 corresponding to the height of the image area 11. Thesubareas 13 are oriented parallel to each other and parallel to thevertical axis of the image 9.

As shown in FIG. 5, each of the subareas 13 is labelled with an index nwith 1≦n≦N, wherein N denotes the total number of the subareas 13 andwherein two neighbouring subareas have successive indices, i.e. theindices n and n+1, for instance. Accordingly, the leftmost subarea 13has the index n=1, a subarea 13 in the middle of the image area 11 hasthe index n=N/2, and the rightmost subarea 13 has the index n=N.

Furthermore, in step X2, for each of the subareas 13, an aggregatedpixel value of the subarea is calculated by aggregating pixel values ofimage pixels contained in the respective subareas 13. In the presentexample, the aggregated pixel value of each one of the subareas 13 isdefined as the sum of the pixel values of the pixels contained in thesubarea 13.

A vector, i.e. a one-dimensional array, of aggregated pixel values whichhas N components is defined and the aggregated pixel values of thesubareas 13, i.e. the pixel sums, are assigned to the components of thevector of aggregated pixel values. The vector of the aggregated pixelvalues has N components such that there is a 1:1-correspondence betweenthese vector components and the subareas. The N components of the vectorof the aggregated pixel values are ordered such that the n-th componentof this vector corresponds to the n-th subarea 13, i.e. the firstcomponent corresponds to the leftmost subarea 13 and the N-th componentto the rightmost subarea 13.

In step X3, for each pair of adjacent subareas of the subareas 13 of theimage area 11, a difference between the aggregated pixel values of thesubareas 13 of the respective pair of adjacent subareas 13 iscalculated. An N−1 dimensional gradient vector is defined and thedifferences between the aggregated pixel values of the pairs of adjacentsubareas 13 are assigned to the N−1 components of the gradient vector.The N−1 components of the gradient vector are ordered such that the n-thcomponent of the gradient vector is defined as the difference betweenthe n-th and the (n+1)-th component of the vector of aggregated pixelvalues.

In the diagram shown in FIG. 6, the values of the components of thegradient vector are represented schematically as a continuous functionof the index n.

In step X4, the gradient vector is analyzed with regard to a predefinedset of possible features of the image area 11. The analysis includesapplying a threshold algorithm to the components of the gradient. In thepresent embodiment, the threshold algorithm includes comparing thecomponents of the gradient vector with a positive threshold and with anegative threshold. In the present example, the positive threshold isset to +100 and the negative threshold is set to −100 as illustrated byhorizontal lines 19 in FIG. 6. Then, a feature vector is defined byapplying a non-maximum suppression algorithm to the output of thethreshold algorithm such that all components of the gradient vectorwhich are neither above the positive threshold nor smaller than thenegative threshold are set to zero. All components of the gradientvector that are above the positive threshold are set to 1 and allcomponents that are smaller than the negative threshold are set to −1.Alternatively, all components of the gradient vector which are neitherlocal maxima nor local minima as well as all components of the gradientvector that are in between the two thresholds could be set to zero,while only those components of the gradient vector that are local maximaand above the positive threshold are set to 1 and only those localminima that are smaller than the negative threshold are set to −1 In thecase illustrated by FIGS. 5 and 6, the gradient vector has three localmaxima 20 that are above the positive threshold line 19 at the indicesn=33, n=109 and n=160, respectively, and three local minima 21 below thenegative threshold line 19 at the indices n=88, n=125 and n=176,respectively. Hence, after application of these algorithms, the valuesof the components of the feature vector will be +1 at the indices n=33,n=109 and n=160, negative at the indices n=88, n=125 and n=176, and zeroelsewhere or, depending of the definition of the step X4, at leasteverywhere except in small intervals around the indices n=33 n=88,n=109, n=125, n=160, and n=176.

The thus obtained feature vector is further analyzed with regard to theoccurrence of one or more predefined possible features of the image area11. This could be done, for example, by means of a support vectormachine and/or applying hierarchical rules. In the present embodiment,the feature array is analyzed by comparing its components with referencedata and, in particular, by checking whether the feature vector showssome particular predefined features which are characteristic for theclass of speed limit signs. These predefined features are defined usinga set of training objects. In the present case, the set of trainingobjects consists of actual speed limit signs that display theabove-mentioned set of numbers. In particular, the term “predefinedfeatures” does not imply that they are fixed once and for all. Even ifthey are predefined for the individual classification procedure, theymay actually be defined dynamically applying machine learningtechniques. In FIG. 6, features of the gradient vector and, thus, of thefeature vector, can be seen for the typical in case where the object 10is the traffic sign of FIG. 4 or a similar speed limit sign.

Characteristic features of the image area 11 which are used for definingthe predefined features of the feature array can be seen in FIG. 5.These characteristic features include, in particular, two first regions14, consisting of pixels which have large pixel values, wherein the twofirst regions 14 each have a first width, and, located in between thetwo first regions 14 and having a second width which is greater than thefirst width, a second region 15 consisting of pixels which have smallpixel values, the first regions 14 and the second region 15 lying withina right half 16 of the image area 11, wherein the right half 16typically corresponds to indices n>N/2, see FIG. 5. These features ofthe image area 11 are used as an indicator that the traffic signcandidate, i.e. the object 10, displays a “0” in its right half whichagain is used as an indicator that the traffic sign belongs to the classof speed limit signs.

In the feature vector, these features result in the followingcharacteristics, which may be used as predefined features foridentifying the object 10 as belonging to the class of speed limitsigns: Two first sequences of components of the feature vector are zero(corresponding to the above mentioned two first regions 14), wherein atthe beginning of each one of these two first sequences there is acomponent of the gradient vector which is +1, and wherein at the end ofeach one of these two first sequences there is a component of thegradient vector which is −1, wherein each of the two sequences has apredefined first width, and wherein, located in between the two firstsequences and having a predefined second width which is greater than thefirst width, there is a second sequence of components (corresponding tothe above mentioned second region 15) having zero values, wherein theindices of the components of the first two sequences and of the secondsequence are all larger than N/2.

In case of the image area 11 shown in FIGS. 4 and 5, these predefinedfeatures are found. Thus, the object 10 will be classified as a speedlimit sign. Furthermore, the components of the gradient vector withindices n<N/2 which correspond to a remaining left half 22 of the imagearea 11 are then analyzed for further features in order to determine theactual speed limit displayed on the speed limit sign.

Thus, in step X5, depending on a result of the analysis of the gradientvector and the feature vector, the traffic sign candidate, i.e. object10, is identified as to either belong to the class of speed limit signsor to belong to the class of all other traffic signs. In the exemplarycase of the object 10 shown in FIG. 4, the object 10 is classified as aspeed limit sign showing the speed limit “80”.

In step S5, the classification of the object 10 by means of the steps X1to X5 is used, for instance, for a further classification of the object10. For instance, if one or more features of the predefined set offeatures of the image area 11 have been found, this is used as anindicator that the object 10 is in fact a traffic sign or belongs to acertain class of traffic signs. For instance, if it has been found insteps X1 to X5 that the traffic sign candidate is no speed limit sign,further classification steps may be performed in step S5. These furthersteps, however, may be omitted if the traffic sign candidate has beenclassified as a speed limit sign by means of steps X1 to X5, as in theexemplary case of the speed limit sign shown in FIG. 4.

LIST OF REFERENCE SIGNS

1 vehicle

2 camera

3 driver assistance system

4 device

5 data processing unit

6 driver information unit

7 display

8 loudspeaker

9 image

10 object

11 image area

12 area defined by the object

13 subarea of image area

14 first region

15 second region

16 right half of the image area

17 ring

18 center

19 threshold line

20 local maximum

21 local minimum

22 left half

1. A method for classifying an object in an image, the method comprisingthe steps of: defining an image area located within the object in theimage, decomposing the image area into an array of subareas, defining anarray of aggregated pixel values by calculating, for each of thesubareas, an aggregated pixel value of the respective subarea,calculating a gradient array depending on differences between theaggregated pixel values of adjacent subareas, analyzing the gradientarray, identifying, depending on a result of the analyzing step, theobject as belonging to a class of a predefined set of classes.
 2. Themethod of claim 1, characterized in that the object in the image is atraffic sign or a candidate for a traffic sign and wherein thepredefined set of classes includes a set of classes of traffic signs. 3.The method of claim 2, characterized in that the set of classes oftraffic signs includes at least one class of speed limit signs, each ofthe speed limit signs of this class displaying a number with one-digitor two-digit or three-digit number.
 4. The method of claim 3,characterized in that it comprises further analyzing the objectidentified as belonging to the at least one class of speed limit signsfor identifying the number displayed on this particular speed limitsign.
 5. The method of claim 1, characterized in that the image area isor comprises a horizontal band.
 6. The method of claim 1, characterizedin that the subareas of the image area are vertical strips.
 7. Themethod of claim 1, characterized in that the aggregated pixel values ofthe subareas are obtained by summing up pixel values of pixels containedin the respective subarea.
 8. The method of claim 1, characterized inthat the analyzing step includes comparing gradient values of thegradient array with at least one threshold value, defining a featurearray by mapping the gradient values onto a smaller set of valuesdepending on whether the respective value of the gradient array islarger or smaller than the at least one threshold value, and analyzingthe feature array.
 9. The method of claim 1, characterized in that theanalyzing step includes comparing features of the gradient array with apredefined set of features.
 10. The method of claim 9, characterized inthat the predefined set of features is obtained from ground truth datausing a training set of objects.
 11. A device for analyzing an objectcontained in an image, comprising a data processing unit configured toperform the steps of the method of claim
 1. 12. A driver assistancesystem, comprising: at least one camera for generating images of asurrounding of a vehicle, the device of claim 11, wherein the dataprocessing unit is coupled to the at least one camera to receive theimages from the at least one camera, and a driver information unitcoupled to the data processing unit and configured to provideinformation on the objects classified as traffic signs and/or speedlimit signs to a driver of the vehicle.
 13. A vehicle comprising thedriver assistance system of claim 12, wherein the at least one camera isinstalled in the vehicle to capture the surrounding of the vehicle infront of the vehicle.